benchmarks.R
In phyr: Model Based Phylogenetic Analysis

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE, eval = FALSE,
  comment = "#>"
)

## -----------------------------------------------------------------------------
# library(phyr)
# 
# # simulate data
# nspp = 500
# nsite = 100
# tree_sim = ape::rtree(n = nspp)
# comm_sim = matrix(rbinom(nspp * nsite, size = 1, prob = 0.6),
#                   nrow = nsite, ncol = nspp)
# row.names(comm_sim) = paste0("site_", 1:nsite)
# colnames(comm_sim) = paste0("t", 1:nspp)
# comm_sim = comm_sim[, tree_sim$tip.label]
# # about 40 times faster
# rbenchmark::benchmark(
#   "picante" = {picante::psv(comm_sim, tree_sim)},
#   "phyr R" = {phyr::psv(comm_sim, tree_sim, cpp = FALSE)},
#   "phyr c++" = {phyr::psv(comm_sim, tree_sim, cpp = TRUE)},
#   replications = 10,
#   columns = c("test", "replications", "elapsed",
#               "relative", "user.self", "sys.self"))
# #>       test replications elapsed relative user.self sys.self
# #> 3 phyr c++           10   0.339    1.000     0.298    0.030
# #> 2   phyr R           10   3.287    9.696     2.907    0.303
# #> 1  picante           10  16.265   47.979    14.824    0.795

## -----------------------------------------------------------------------------
# comm_sim = matrix(rpois(nspp * nsite, 3), nrow = nsite, ncol = nspp)
# row.names(comm_sim) = paste0("site_", 1:nsite)
# colnames(comm_sim) = paste0("t", 1:nspp)
# comm_sim = comm_sim[, tree_sim$tip.label]
# # about 2-3 times faster
# rbenchmark::benchmark(
#   "picante" = {picante::pse(comm_sim, tree_sim)},
#   "phyr R" = {phyr::pse(comm_sim, tree_sim, cpp = FALSE)},
#   "phyr c++" = {phyr::pse(comm_sim, tree_sim, cpp = TRUE)},
#   replications = 20,
#   columns = c("test", "replications", "elapsed",
#               "relative", "user.self", "sys.self"))
# #>       test replications elapsed relative user.self sys.self
# #> 3 phyr c++           20   1.456    1.000     1.329    0.105
# #> 2   phyr R           20   4.233    2.907     3.453    0.555
# #> 1  picante           20   3.858    2.650     3.319    0.475

## ----message=FALSE------------------------------------------------------------
# # pcd is about 20 times faster
# rbenchmark::benchmark(
#   "phyr" = {phyr::pcd(comm = comm_a, tree = phylotree, reps = 1000, verbose = FALSE)},
#   "picante" = {picante::pcd(comm = comm_a, tree = phylotree, reps = 1000)},
#   replications = 10,
#   columns = c("test", "replications", "elapsed",
#               "relative", "user.self", "sys.self"))
# #>      test replications elapsed relative user.self sys.self
# #> 1    phyr           10   0.214    1.000     0.192    0.012
# #> 2 picante           10   4.516   21.103     4.043    0.074

## -----------------------------------------------------------------------------
# library(ape)
# # Set up parameter values for simulating data
# n <- 50
# phy <- rcoal(n, tip.label = 1:n)
# trt_names <- paste0("par", 1:2)
# 
# R <- matrix(c(1, 0.7, 0.7, 1), nrow = 2, ncol = 2)
# d <- c(0.3, 0.95)
# B2 <- 1
# 
# Se <- c(0.2, 1)
# M <- matrix(Se, nrow = n, ncol = 2, byrow = TRUE)
# colnames(M) <- trt_names
# 
# # Set up needed matrices for the simulations
# p <- length(d)
# 
# star <- stree(n)
# star$edge.length <- array(1, dim = c(n, 1))
# star$tip.label <- phy$tip.label
# 
# Vphy <- vcv(phy)
# Vphy <- Vphy/max(Vphy)
# Vphy <- Vphy/exp(determinant(Vphy)$modulus[1]/n)
# 
# tau <- matrix(1, nrow = n, ncol = 1) %*% diag(Vphy) - Vphy
# C <- matrix(0, nrow = p * n, ncol = p * n)
# for (i in 1:p) for (j in 1:p) {
#   Cd <- (d[i]^tau * (d[j]^t(tau)) * (1 - (d[i] * d[j])^Vphy))/(1 - d[i] * d[j])
#   C[(n * (i - 1) + 1):(i * n), (n * (j - 1) + 1):(j * n)] <- R[i, j] * Cd
# }
# MM <- matrix(M^2, ncol = 1)
# V <- C + diag(as.numeric(MM))
# 
# iD <- t(chol(V))
# 
# XX <- iD %*% rnorm(2 * n)
# X <- matrix(XX, n, p)
# colnames(X) <- trt_names
# rownames(X) <- phy$tip.label
# rownames(M) <- phy$tip.label
# 
# U <- list(cbind(rnorm(n, mean = 2, sd = 10)))
# names(U) <- trt_names[2]
# 
# X[,2] <- X[,2] + B2[1] * U[[1]][,1] - B2[1] * mean(U[[1]][,1])
# 
# z <- cor_phylo(variates = X,
#                covariates = U,
#                meas_errors = M,
#                phy = phy,
#                species = phy$tip.label)
# 
# 
# U2 <- list(NULL, matrix(rnorm(n, mean = 2, sd = 10), nrow = n, ncol = 1))
# rownames(U2[[2]]) <- phy$tip.label
# colnames(U2[[2]]) <- "par2"
# X2 = X
# X2[,2] <- X2[,2] + B2[1] * U2[[2]][,1] - B2[1] * mean(U2[[2]][,1])
# 
# z_r <- corphylo(X = X2, SeM = M, U = U2, phy = phy, method = "Nelder-Mead")
# 
# rbenchmark::benchmark(
#   "cor_phylo" = {cor_phylo(variates = X, covariates = U, meas_errors = M,
#                            phy = phy, species = phy$tip.label)},
#   "corphylo" = {corphylo(X = X2, SeM = M, U = U2, phy = phy, method = "Nelder-Mead")},
#   replications = 5,
#   columns = c("test", "replications", "elapsed",
#               "relative", "user.self", "sys.self")
# )
# #>        test replications elapsed relative user.self sys.self
# #> 1 cor_phylo            5   4.511    1.000     4.329    0.062
# #> 2  corphylo            5  16.190    3.589    13.863    1.369